Description of your vignette
BayesianAPP is a package for automatically download KEGG pathway data (kgml file) and
The BayesianAPP package utilize Pathway prioritization approach from the following publication. Please try to cite the article when your publication result obtained by using this software.
Add citation
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BayesianAPP")
Example data were simulated from GSE20685. Simulation details include indiviudal random sampling and expression level shifting and scaling for each gene.
library(BayesianAPP)
data("BayesianAPP.example")
attach(BayesianAPP.example)
Simulated gene expression data contains header. The first column is ENTREZ gene ID, the second column is corresponding gene symbol and the other fields are gene expression value for each individual.
head(GE_data[,seq(5)])
## ENTREZ_GENE_ID Gene.Symbol case_1 case_2 case_3
## 1 1 A1BG 8.8239 8.6329 7.6936
## 2 2 A2M 12.4704 12.7797 12.5137
## 3 3 A2MP1 6.6183 4.7546 4.2940
## 4 9 NAT1 15.1912 11.5157 14.2173
## 5 10 NAT2 3.1079 4.3559 2.9828
## 6 12 SERPINA3 13.7560 9.9250 9.7697
Simulated phenotype data contains 50 cases and 50 controls with header. The first column is sample ID and the second is status/phenotype.
head(pheno_data)
## ID Status
## 1 case_1 1
## 2 case_2 1
## 3 case_3 1
## 4 case_4 1
## 5 case_5 1
## 6 case_6 1
tail(pheno_data)
## ID Status
## 95 control_45 0
## 96 control_46 0
## 97 control_47 0
## 98 control_48 0
## 99 control_49 0
## 100 control_50 0
table(pheno_data$Status)
##
## 0 1
## 50 50
library(BayesianAPP)
data("BayesianAPP.example")
attach(BayesianAPP.example)
download_kgml(pathwayid = c("04115", "04915", "04630"),
species = "hsa",
dir = getwd(),
download_subpathway = TRUE,
gmx = TRUE)
p <- plotly_refgene(file = GE_data,
pathwayid = "04115",
species = "hsa")
## D:/BayesianAPP/BayesianAPP/plotly_hsa04115/hsa04115_pathway.html is made.
## D:/BayesianAPP/BayesianAPP/plotly_hsa04115/hsa04115_barplot.html is made.
## D:/BayesianAPP/BayesianAPP/plotly_hsa04115/hsa04115_combined.html is made.
# A list of plotly output
p[[1]]
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# It is also allowed to input multiple pathways.
plotly_refgene(file = GE_data,
pathwayid = c("04115", "04915", "04630"),
species = "hsa")
# Perform BayesianAPP analysis
# file_gmx is the file generated by download_kgml()
# Please run R/RStudio as administrator or it may not work out.
output <- BayesianAPP(dir = getwd(),
file_ge = GE_data,
file_pheno = pheno_data,
file_gmx = c("hsa04115_entrezid.gmx",
"hsa04915_entrezid.gmx",
"hsa04630_entrezid.gmx"),
ref_gene = c("7157","1956","116379"),
out_type = "D",
model_file = "my_model.odc",
n.iter = 6000,
n.chains = 1,
n.burnin = 1000,
n.thin = 10)
# Display rank of competeting pathways
output$rank.table
## Pathway Rank Posterior.prob
## 1 hsa04630_entrezid.gmx 1 1.0000
## 2 hsa04915_entrezid.gmx 2 0.9966
## 3 hsa04115_entrezid.gmx 3 0.7604
# visualization of density of beta coefficients for each pathway
plot.BayesianAPP(output)
This analysis was conducted on:
sessionInfo()
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Traditional)_Taiwan.950
## [2] LC_CTYPE=Chinese (Traditional)_Taiwan.950
## [3] LC_MONETARY=Chinese (Traditional)_Taiwan.950
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Traditional)_Taiwan.950
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] BayesianAPP_0.1.0 R2OpenBUGS_3.2-3.2 purrr_0.3.3
## [4] plotly_4.9.1 ggplot2_3.2.1 pathview_1.26.0
## [7] org.Hs.eg.db_3.10.0 AnnotationDbi_1.48.0 IRanges_2.20.2
## [10] S4Vectors_0.24.3 Biobase_2.46.0 BiocGenerics_0.32.0
## [13] matrixStats_0.55.0 data.table_1.12.8 knitr_1.28
## [16] BiocStyle_2.14.4
##
## loaded via a namespace (and not attached):
## [1] fastmatch_1.1-0 BiocFileCache_1.10.2 plyr_1.8.5
## [4] igraph_1.2.4.2 lazyeval_0.2.2 splines_3.6.2
## [7] BiocParallel_1.20.1 crosstalk_1.0.0 sva_3.34.0
## [10] urltools_1.7.3 digest_0.6.23 htmltools_0.4.0
## [13] GOSemSim_2.12.0 viridis_0.5.1 GO.db_3.10.0
## [16] magrittr_1.5 memoise_1.1.0 limma_3.42.2
## [19] Biostrings_2.54.0 annotate_1.64.0 graphlayouts_0.5.0
## [22] askpass_1.1 enrichplot_1.6.1 prettyunits_1.1.1
## [25] colorspace_1.4-1 blob_1.2.1 rappdirs_0.3.1
## [28] ggrepel_0.8.1 xfun_0.12 dplyr_0.8.4
## [31] crayon_1.3.4 RCurl_1.98-1.1 jsonlite_1.6.1
## [34] graph_1.64.0 genefilter_1.68.0 survival_3.1-8
## [37] glue_1.3.1 polyclip_1.10-0 gtable_0.3.0
## [40] zlibbioc_1.32.0 XVector_0.26.0 Rgraphviz_2.30.0
## [43] scales_1.1.0 DOSE_3.12.0 pheatmap_1.0.12
## [46] DBI_1.1.0 Rcpp_1.0.3 viridisLite_0.3.0
## [49] xtable_1.8-4 progress_1.2.2 gridGraphics_0.4-1
## [52] bit_1.1-15.2 europepmc_0.3 htmlwidgets_1.5.1
## [55] httr_1.4.1 fgsea_1.12.0 RColorBrewer_1.1-2
## [58] pkgconfig_2.0.3 XML_3.99-0.3 farver_2.0.3
## [61] dbplyr_1.4.2 labeling_0.3 later_1.0.0
## [64] ggplotify_0.0.4 tidyselect_1.0.0 rlang_0.4.4
## [67] reshape2_1.4.3 munsell_0.5.0 tools_3.6.2
## [70] RSQLite_2.2.0 ggridges_0.5.2 fastmap_1.0.1
## [73] evaluate_0.14 stringr_1.4.0 yaml_2.2.1
## [76] bit64_0.9-7 tidygraph_1.1.2 KEGGREST_1.26.1
## [79] dendextend_1.13.3 ggraph_2.0.1 nlme_3.1-144
## [82] mime_0.9 KEGGgraph_1.46.0 DO.db_2.9
## [85] xml2_1.2.2 biomaRt_2.42.0 MAGeCKFlute_1.6.3
## [88] compiler_3.6.2 curl_4.3 png_0.1-7
## [91] ggsignif_0.6.0 tibble_2.1.3 tweenr_1.0.1
## [94] stringi_1.4.4 lattice_0.20-38 Matrix_1.2-18
## [97] ggsci_2.9 vctrs_0.2.2 pillar_1.4.3
## [100] lifecycle_0.1.0 BiocManager_1.30.10 triebeard_0.3.0
## [103] cowplot_1.0.0 bitops_1.0-6 httpuv_1.5.2
## [106] qvalue_2.18.0 R6_2.4.1 promises_1.1.0
## [109] bookdown_0.17 gridExtra_2.3 boot_1.3-24
## [112] MASS_7.3-51.5 assertthat_0.2.1 openssl_1.4.1
## [115] withr_2.1.2 mgcv_1.8-31 hms_0.5.3
## [118] clusterProfiler_3.14.3 grid_3.6.2 tidyr_1.0.2
## [121] coda_0.19-3 rmarkdown_2.1 rvcheck_0.1.7
## [124] ggpubr_0.2.4 ggforce_0.3.1 shiny_1.4.0
## [127] base64enc_0.1-3
Lin, S. J., Lu, T. P., Yu, Q. Y., Hsiao, C. K. (2018). Probabilistic prioritization of candidate pathway association with pathway score. BMC bioinformatics, 19(1), 391. https://doi.org/10.1186/s12859-018-2411-z